code: def test_autocast(self):
      assertEqual = self.assertEqual
  
      # A layer with a int dtype (some preprocessing layers do this).
      class InnerLayerOne(layers.Layer):
          def __init__(self):
              super().__init__(dtype="int")
              self.v = self.add_weight(
                  shape=(),
                  initializer="ones",
                  trainable=True,
                  dtype="float32",
              )
              self.built = True
  
          def call(self, x):
              # Should not autocast.
              assertEqual(
                  backend.standardize_dtype(self.v.dtype), "float32"
              )
              return ops.cast(x, "float32") + self.v
  
      # A layer that is explicitly full precision.
      class InnerLayerTwo(layers.Layer):
          def __init__(self):
              super().__init__(dtype="float32")
              self.v = self.add_weight(
                  shape=(),
                  initializer="ones",
                  trainable=True,
              )
              self.built = True
  
          def call(self, x):
              # Should not autocast.
              assertEqual(
                  backend.standardize_dtype(self.v.dtype), "float32"
              )
              return x + self.v
  
      # A layer that is explicitly mixed precision with inner layers.
      class MixedPrecisionLayer(layers.Layer):
          def __init__(self):
              super().__init__(dtype="mixed_float16")
              self.v = self.add_weight(
                  shape=(),
                  initializer="ones",
                  trainable=True,
              )
              self.inner_one = InnerLayerOne()
              self.inner_two = InnerLayerTwo()
              self.built = True
  
          def call(self, x):
              # Should autocast.
              assertEqual(
                  backend.standardize_dtype(self.v.dtype), "float16"
              )
              return self.inner_two(self.inner_one(x + self.v))
  
      layer = MixedPrecisionLayer()
      y = layer(np.array(0.0))
      self.assertEqual(y, 3.0)
  
imports: import numpy
  import pytest
  from keras import backend
  from keras import layers
  from keras import metrics
  from keras import models
  from keras import ops
  from keras import testing
label_desc: Use image node_id 11 for calling the LayerTest obj's underlying member method code with example usage: obj.test_autocast() and returns: unknown, unknown, self